Will Big Data Drive Big Insights or Big Costs?

by Braden Kelley

IBM today is announcing a broad portfolio of new software that applies its expertise in analytics in a new way to the infrastructure of an organization.

Now that we are surrounded by data and organizations are accumulating more and more data about their operations, their customers, their processes and more, analytics can be applied for the purpose of identifying hidden patterns and using that knowledge to gain industry insight. Whether it’s financial opportunities, traffic patterns, medical treatments, or even science breakthroughs, organizations are finding smart ways to harness this flood of data and make sense of it in real time to make predictions and take actions.

Data may not be sexy, but having done a lot of work to extract insights from large amounts of data for organizations like Homeaway.com and Microsoft Windows Live I know first hand that with the right mindset and the right tools, you can turn data into information, information into knowledge, knowledge into insights, and insights into innovation.

Data is just the beginning of that transformation from data into innovation, but a lot of great innovations have come from identifying anomalies and connecting unexpected dots in the data.

Data can even sometimes help you identify where the problems and unmet needs lie, those that we are always hoping to find to drive innovation, but you’ll only be able to find them if you are willing to look and know where to look and if you have the right data for the purpose.

The trouble is that not all data is ones and zeroes, nor should it be, and that many organizations focus on gathering every conceivable piece of data instead of being strategic in their data gathering, so that they can be strategic in their data analytics. The key is not how much data you have but how much actionable data you have. When I work with clients and their data to extract insights, I always ask:

Why do you want to know that?

What are you going to do with that information?

What action will you take or not take based on the answer to that data request?

You must differentiate between interesting data and actionable data. Believe it or not, it is possible to gather too much data, especially if you are a big company with millions of customers. Gathering too much data on millions of customers can result in analytics tasks that take too long to complete to be actionable or even useful, so proceed with caution.

Another risk to be aware of when it comes to data, insights and innovation is that some companies tend to rely too much on the data they can easily measure, that’s binary or numeric, and miss the true insights that lie beneath. To combat this and to make your innovation sustainable, I encourage you to build a global sensing network to give you a well-rounded data set to give you lots of different views on your innovation possibilities. Make your actionable big data bigger, your interesting big data smaller, swing for the fences in your pursuit of big insights, and make sure that your data pursuits don’t result in big costs but instead in big sources of innovation.

Finally, it’s a bit ironic that in the middle of writing this article I got the blue screen of death from Windows 7. I’m not sure quite what to make of that, but one thing is for sure – everybody needs a backup. Have you made yours recently? 😉

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Braden,
Your focus on “What action will you take or not take based on the answer to that data request?” reminds me of a favorite line from a decision science prof: “The value of information that won’t drive action is zero.” [He has a small caveat that if it helped you sleep better, it might be worth something to you…but not your organization.] As you suggest, that value isn’t really zero–it’s negative. It costs us to store, manage, and weed through information, whether we use to drive action or not!

On these Big Data lines, realizing value gets derailed when people talk about Big Data with a technical mindset, for example, defining data as “Big” when it’s above a certain size or flows at a high enough rate. I believe the best way to think about Big Data is New Meaning from New Sources. If interested, here’s our take on it…as well as what we see as a misguided definition from eWeek and Gartner:http://www.perworks.com/eweek-gartner-got-big-data-wrong/